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Topological structure prediction in binary nanoparticle superlattices
Author(s) -
Alex Travesset
Publication year - 2016
Publication title -
soft matter
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 170
eISSN - 1744-6848
pISSN - 1744-683X
DOI - 10.1039/c6sm00713a
Subject(s) - superlattice , topology (electrical circuits) , binary number , nanoparticle , orbifold , ligand (biochemistry) , materials science , stability (learning theory) , physics , chemical physics , chemistry , condensed matter physics , nanotechnology , theoretical physics , mathematics , computer science , combinatorics , arithmetic , receptor , biochemistry , machine learning
Systems of spherical nanoparticles with capping ligands have been shown to self-assemble into beautiful superlattices of fascinating structure and complexity. In this paper, I show that the spherical geometry of the nanoparticle imposes constraints on the nature of the topological defects associated with the capping ligand and that such topological defects control the structure and stability of the superlattices that can be assembled. All these considerations form the basis for the orbifold topological model (OTM) described in this paper. The model quantitatively predicts the structure of super-lattices where capping ligands are hydrocarbon chains in excellent agreement with experimental results, explains the appearance of low packing fraction lattices as equilibrium, why certain similar structures are more stable (bccAB 6 vs. CaB 6 , AuCu vs. CsCl, etc.) and many other experimental observations.

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